Botrytis cinerea is a necrotic plant fungus that causes gray mold disease in over 200 crops, including grapevine. Due to its genetic plasticity, this fungus presents strong resistance to many fungicides. Thus, new strategies against B. cinerea are urgently needed. In this context, antimicrobial photodynamic treatment (APDT) was considered. APDT involves the use of a photosensitizer that generates reactive oxygen species upon illumination with white light. Tetra-4-sulfonatophenyl porphyrin tetra-ammonium (TPPS) was tested on B. cinerea using light. 1.5 ?M TPPS completely inhibited mycelial growth. TPPS (12.5 ?M) was tested on three grapevine clones from Chardonnay, Merlot and Sauvignon, grown in vitro for 2 months. Treated root apparatus of the three backgrounds increased thiol production as a molecular protection against photoactivated TPPS, leading to a normal phenotype as compared with control plantlets. Finally, 2-month-old grapevine leaves were infected with 4-day-old mycelium of B. cinerea pre-incubated or not with TPPS. The pre-treated mycelium was unable to infect the detached leaves of any of the three grapevine varieties after 72 h growth when subjected to a 16 h photoperiod, contrary to untreated mycelium. These results suggest a strong potential of photo-treatment against B. cinerea mycelium for future agricultural practices in vineyard or other cultures.Lung cancer is the leading cause of cancer death worldwide. The Xuanwei-Fuyuan (XF) region of Yunnan, China has a high incidence of lung cancer from coal-related pollution. Effort to raise public awareness screening for lung cancer has been ongoing. We retrospectively analyzed overall survival (OS) of lung cancer patients of a tertiary cancer center in Yunnan to investigate screening and regional residential status as predictive factors. Consecutive cases of newly diagnosed lung cancer were reviewed. The lung cancer cases diagnosed by screening were more likely to be early-staged and treated by surgery than those diagnosed not by screening. In patients diagnosed not by screening, XF residential status was a significant predictor of improved OS. Frailty model detected significant heterogeneity associated with region of residence in unscreened patients. Potential biases associated with screening were examined by Monte Carlo simulations and sensitivity analyses. Focused effort in cancer screening and increased public awareness of pollution-related lung cancer in XF might have led to early diagnosis and improved OS, and increased investment in health care resources in high risk areas may have produced additional unobserved factors that underlay the association of XF residential status with improved OS in patients diagnosed not by screening.We aim to study the association of hyperlipidemia and statin use with COVID-19 severity. We analysed a retrospective cohort of 717 patients admitted to a tertiary centre in Singapore for COVID-19 infection. Clinical outcomes of interest were oxygen saturation???94% requiring supplemental oxygen, intensive-care unit (ICU) admission, invasive mechanical-ventilation and death. Patients on long term dyslipidaemia medications (statins, fibrates or ezetimibe) were considered to have dyslipidaemia. Logistic regression models were used to study the association between dyslipidaemia and clinical outcomes adjusted for age, gender and ethnicity. Statin treatment effect was determined, in a nested case-control design, through logistic treatment models with 13 propensity matching for age, gender and ethnicity. All statistical tests were two-sided, and statistical significance was taken as p? less then ?0.05. One hundred fifty-six (21.8%) patients had dyslipidaemia and 97% of these were on statins. Logistic treatment models showed a lower chance of ICU admission for statin users when compared to non-statin users (ATET Coeff (risk difference)?-?0.12 (-?0.23,?-?0.01); p?=?0.028). There were no other significant differences in other outcomes. Statin use was independently associated with lower ICU admission. This supports current practice to continue prescription of statins in COVID-19 patients.Ultrafast Tm-doped fibre lasers have been actively studied for the last decade due to their potential applications in precise mid-IR spectroscopy, LIDARs, material processing and more. The majority of research papers is devoted to the comparison between a numerical modelling and experimental results; however, little attention is being paid to the comprehensive description of the mathematical models and parameters of the active and passive components forming cavities of Tm-doped all-fibre lasers. Thus, here we report a numerical model of a stretched-pulsed Tm-doped fibre laser with hybrid mode-locking and compare it with experimental results. The key feature of the developed numerical model is employment of the experimentally measured dispersion coefficients and optimisation of some model parameters, such as the bandwidth of the spectral filter spectral filtering and the saturation power of the active fibre, for a conformity with the experiment. The developed laser emits 331.7 fs pulses with a 23.8 MHz repetition rate, 6 mW of average power, 0.25 nJ of pulse energy, and a 21.66 nm spectral bandwidth at a peak wavelength of 1899.5 nm. The numerical model characteristics coincide with experimentally achieved spectral width, pulse duration, and average power with inaccuracy of 4.7%, 5.4%, and 22.9%, respectively. Moreover, in the discussion of the work the main possible reasons influencing this inaccuracy are highlighted. Elimination of those factors might allow to increase accuracy even more. We show that numerical model has a good agreement with the experiment and can be used for development of ultrafast Tm-doped fibre laser systems.Acute lower respiratory infection is the leading cause of child death in developing countries. Current strategies to reduce this problem include early detection and appropriate treatment. https://www.selleckchem.com/products/GDC-0980-RG7422.html Better diagnostic and therapeutic strategies are still needed in poor countries. Artificial-intelligence chest X-ray scheme has the potential to become a screening tool for lower respiratory infection in child. Artificial-intelligence chest X-ray schemes for children are rare and limited to a single lung disease. We need a powerful system as a diagnostic tool for most common lung diseases in children. To address this, we present a computer-aided diagnostic scheme for the chest X-ray images of several common pulmonary diseases of children, including bronchiolitis/bronchitis, bronchopneumonia/interstitial pneumonitis, lobar pneumonia, and pneumothorax. The study consists of two main approaches first, we trained a model based on YOLOv3 architecture for cropping the appropriate location of the lung field automatically. Second, we compared three different methods for multi-classification, included the one-versus-one scheme, the one-versus-all scheme and training a classifier model based on convolutional neural network.